{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_media(iflaga):\n",
    "    global nx, ny, mxst, mxnd, myst, mynd, mediaEz, mediaHx, mediaHy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    if (iflaga == 2):\n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-1):\n",
    "                    if ( j >= myst-1) and (j <= mynd-1):\n",
    "                        mediaEz[i,j] = 2\n",
    "        \n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-1):\n",
    "                    if ( j >= myst-1) and (j <= mynd-2):\n",
    "                        mediaHx[i,j] = 2\n",
    "\n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-2):\n",
    "                    if ( j >= myst-1) and (j <= mynd-1):\n",
    "                        mediaHy[i,j] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_coefficients():\n",
    "    global Ca, Cb, Da, Db # Define material based coefficients\n",
    "    global xmu, eps0, dt, ds\n",
    "    # Field Coefficients\n",
    "    dte = dt/(ds*eps0)\n",
    "    dtm = dt/(ds*xmu)\n",
    "    Da[0] = 1\n",
    "    Db[0] = dtm\n",
    "    Ca[0] = 1\n",
    "    Cb[0] = dte\n",
    "    dte1 = dt/(ds*eps0*4)\n",
    "    dtm1 = dt/(ds*xmu)\n",
    "    Da[1] = 1\n",
    "    Db[1] = dtm1\n",
    "    Ca[1] = 1\n",
    "    Cb[1] = dte1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Source(n, sources):\n",
    "    global Ezs\n",
    "    # Creates a half-sinusoidal source between the time increments\n",
    "    # 1 and 10.%\n",
    "    # When source = 1 : Sinusoid\n",
    "    #               2 : Gaussian\n",
    "    #\n",
    "    ## For Gaussian Source\n",
    "    if sources == 2:\n",
    "            xndec = 10.0\n",
    "            xn0 = 4*xndec\n",
    "            Ezs = math.exp(-((n-xn0)/(xndec))**2)\n",
    "        ## For Sinusoidal Source\n",
    "    elif sources == 1:\n",
    "        if ( n >=1-1) and (n <= 10-1):\n",
    "            Ezs = math.sin(n*math.pi/10)\n",
    "    return Ezs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adv_Ez(n, sources):\n",
    "    global Ez, Hx, Hy\n",
    "    global mediaEz\n",
    "    global Ca, Cb\n",
    "    global nx, ny\n",
    "    for i in range(nx):\n",
    "        for j in range(ny):\n",
    "            m  = int(mediaEz(i,j)-1)\n",
    "            if (i == 6):  # Incident Field Source Excitation\n",
    "                Es = Source(n, sources) # Es is a soft source\n",
    "            else:\n",
    "                Es = 0\n",
    "            if (i >= 1 and j >=1):\n",
    "                Ez[i,j] = Ez[i,j]*Ca[m] + Cb[m]*(Hy[i,j] - Hy[i-1,j]- (Hx[i,j] - Hx[i,j-1])) + Es\n",
    "            elif (i >= 2 and j == 1): # Field at the bottom edge of the boundary\n",
    "                Ez[i,j] = Ez[i,j]*Ca[m] + Cb[m]*(Hy[i,j] - Hy[i-1,j]- Hx(i,j)) + Es"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adv_H(n):\n",
    "    global Ez, Hx, Hy\n",
    "    global mediaHx, mediaHy\n",
    "    global Da, Db\n",
    "    global nx, ny\n",
    "    for i in range(nx):\n",
    "        for j in range(ny-1):\n",
    "            m = int(mediaHx[i, j]-1)\n",
    "            Hx[i, j] = Hx[i, j]*Da[m] - Db[m]*(Ez[i, j+1] - Ez[i, j])\n",
    "    for i in range(nx-1):\n",
    "        for j in range(ny):\n",
    "            m = int(mediaHy[i, j]-1)\n",
    "            Hy[i, j] = Hy[i, j]*Da[m] + Db[m]*(Ez[i+1, j] - Ez[i, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_surface_plot(field):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    xd = np.linspace(0, 10, 100)\n",
    "    yd = np.linspace(0, 10, 80)\n",
    "    [xdg, ydg] = np.meshgrid(xd, yd)\n",
    "    dem3d = ax.plot_surface(xdg, ydg, field, cmap='rainbow', edgecolor='none')\n",
    "    fig.colorbar(dem3d, shrink=0.5, aspect=5)\n",
    "\n",
    "\n",
    "def my_line_plot(Ez_5, Ez_15, Ez_25, Ez_35, Ez_45, Ez_55, Ez_65, Ez_75, Ez_85, Ez_95, iflaga, source):\n",
    "    maxval=1.4\n",
    "    fig = plt.figure(figsize=(6,6))\n",
    "    plt.subplot(10, 1, 1)\n",
    "    plt.plot(range(len(Ez_5)), Ez_5, 'k', label='n=5')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 2)\n",
    "    plt.plot(range(len(Ez_15)), Ez_15, 'k', label='n=15')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 3)\n",
    "    plt.plot(range(len(Ez_25)), Ez_25, 'k', label='n=25')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 4)\n",
    "    plt.plot(range(len(Ez_35)), Ez_35, 'k', label='n=35')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 5)\n",
    "    plt.plot(range(len(Ez_45)), Ez_45, 'k', label='n=45')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 6)\n",
    "    plt.plot(range(len(Ez_55)), Ez_55, 'k', label='n=55')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 7)\n",
    "    plt.plot(range(len(Ez_65)), Ez_65, 'k', label='n=65')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 8)\n",
    "    plt.plot(range(len(Ez_75)), Ez_75, 'k', label='n=75')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 9)\n",
    "    plt.plot(range(len(Ez_85)), Ez_85, 'k', label='n=85')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    plt.xticks([])\n",
    "    plt.subplot(10, 1, 10)\n",
    "    plt.plot(range(len(Ez_95)), Ez_95, 'k', label='n=95')\n",
    "    plt.legend(loc=1)\n",
    "    plt.xlim([0, 80])\n",
    "    #plt.xticks([])\n",
    "    if iflaga == 1 and source == 1:\n",
    "        fig.suptitle('Line Plots for E-field with no obstacle for a sinusoid source')\n",
    "    elif iflaga == 2 and source == 1:\n",
    "        fig.suptitle('Line Plots for E-field with a dielectric box for a sinusoid source, strip = %d' % strip)\n",
    "    elif source == 2:\n",
    "        fig.suptitle('Line Plots for E-field with no obstacle for a Gaussian source')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Routine to zero out the global variables\n",
    "#*************************\n",
    "# *************************\n",
    "def zeroing():\n",
    "# Clears but retains the variables in memory\n",
    "\n",
    "    global nx,  ny\n",
    "    global Ez,  Hx,  Hy # Create E and H field components.\n",
    "    # global data type enables the global scope of the variables\n",
    "    # within the code. Unlike global variables they can not be accessed\n",
    "    #outside the code\n",
    "    global mediaEz, mediaHx, mediaHy\n",
    "    global Ca, Cb, Da, Db # Define material based coefficients\n",
    "\n",
    "\n",
    "    Ez = np.zeros((nx,ny)) # z-component of E-field\n",
    "    Hx = np.zeros((nx,ny)) # x-component of H-field\n",
    "    Hy = np.zeros((nx,ny)) # y-component of H-field\n",
    "\n",
    "    mediaEz = np.ones((nx,ny)) # z-component of E-field\n",
    "    mediaHx = np.ones((nx,ny)) #x-component of H-field\n",
    "    mediaHy = np.ones((nx,ny)) # x-component of H-field\n",
    "\n",
    "    Ca = np.zeros((2,1)) # x-component of H-field\n",
    "    Cb = np.zeros((2,1)) # x-component of H-field\n",
    "    Da = np.zeros((2,1)) # x-component of H-field\n",
    "    Db = np.zeros((2,1)) # x-component of H-field\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_ex4():\n",
    "    ############## Parameters\n",
    "    global c, xmu, eps0, asize\n",
    "    global nx, ny, nt, mxst, mxnd, myst, mynd\n",
    "    global dt, ds\n",
    "    global Ez, Hx, Hy #Create E and H field components.\n",
    "    # global data type enables the global scope of the variables\n",
    "    # within the code.Unlike global variables they can not be accessed\n",
    "    # outside the code\n",
    "    global mediaEz, mediaHx, mediaHy\n",
    "    global Ca, Cb, Da, Db # Define material based coefficients\n",
    "    global Ezs # The excitation signal\n",
    "    global strip\n",
    "\n",
    "    c = 2.99792458e8\n",
    "    xmu = 4 * math.pi * 10**(-7)\n",
    "    eps0 = 8.854187817*10**(-12)\n",
    "    asize = 5\n",
    "    nx = 80\n",
    "    ny = 100\n",
    "    nt = 100\n",
    "    mxst = 17\n",
    "    mxnd = 49\n",
    "    myst = 33\n",
    "    mynd = 65\n",
    "    strip = 30\n",
    "\n",
    "    Ez = np.zeros((nx, ny),dtype='longdouble')\n",
    "    Hx = np.zeros((nx, ny),dtype='longdouble')\n",
    "    Hy = np.zeros((nx, ny),dtype='longdouble')\n",
    "\n",
    "    mediaEz = np.ones((nx, ny),dtype='longdouble')\n",
    "    mediaHx = np.ones((nx, ny),dtype='longdouble')\n",
    "    mediaHy = np.ones((nx, ny),dtype='longdouble')\n",
    "\n",
    "    Ca = np.zeros((2, 1),dtype='longdouble')\n",
    "    Cb = np.zeros((2, 1),dtype='longdouble')\n",
    "    Da = np.zeros((2, 1),dtype='longdouble')\n",
    "    Db = np.zeros((2, 1),dtype='longdouble')\n",
    "\n",
    "\n",
    "    ds = asize / (mxnd - mxst - 1)\n",
    "    dt = ds / (c * math.sqrt(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_ex4()\n",
    "iflaga = 1  # PEC box\n",
    "source = 1  # sinusoid source\n",
    "strip = 30\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for x in range(nt):\n",
    "    n = x + 1\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    if n == 5:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 15:\n",
    "        Ez_15 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 25:\n",
    "        Ez_25 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 35:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 45:\n",
    "        Ez_45 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 55:\n",
    "        Ez_55 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 65:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 75:\n",
    "        Ez_75 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 85:\n",
    "        Ez_85 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 95:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_15, Ez_25, Ez_35, Ez_45, Ez_55, Ez_65, Ez_75, Ez_85, Ez_95, iflaga, source)\n",
    "zeroing()\n",
    "\n",
    "iflaga = 1  # PEC box\n",
    "source = 1  # sinusoid source\n",
    "strip = 50\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for x in range(nt):\n",
    "    n = x + 1\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    if n == 5:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 15:\n",
    "        Ez_15 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 25:\n",
    "        Ez_25 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 35:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 45:\n",
    "        Ez_45 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 55:\n",
    "        Ez_55 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 65:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 75:\n",
    "        Ez_75 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 85:\n",
    "        Ez_85 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 95:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_15, Ez_25, Ez_35, Ez_45, Ez_55, Ez_65, Ez_75, Ez_85, Ez_95, iflaga, source)\n",
    "zeroing()\n",
    "\n",
    "iflaga = 1  # PEC box\n",
    "source = 1  # sinusoid source\n",
    "strip = 65\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for n in range(nt):\n",
    "    \n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    if n == 5:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 15:\n",
    "        Ez_15 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 25:\n",
    "        Ez_25 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 35:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 45:\n",
    "        Ez_45 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 55:\n",
    "        Ez_55 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 65:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 75:\n",
    "        Ez_75 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 85:\n",
    "        Ez_85 = copy.deepcopy(Ez[:, strip])\n",
    "    elif n == 95:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_15, Ez_25, Ez_35, Ez_45, Ez_55, Ez_65, Ez_75, Ez_85, Ez_95, iflaga, source)\n",
    "zeroing()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "db5e7618818223b409914352f6f17f673f33d2d3eabd9e64ff7d3a59fcd514ed"
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  "kernelspec": {
   "display_name": "Python 3.9.11 ('ml')",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
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}
